#variationalquantumeigensolver

Physics-Aware AI and Quantum Mechanics Meet in 2025's Computational Revolution In 2025, quantum mechanics

Quantum mechanics' centenary in 2025 honors Werner Heisenberg and his colleagues' 1925 contributions to quantum theory. What began as an attempt to explain blackbody radiation and hydrogen atom energy levels is now the foundation of modern technology. Erwin Schrödinger's wave equation and Niels Bohr's atomic model revolutionized matter and made the transistor, the heart of digital computing, conceivable.

A second quantum revolution is underway. The “Era of Utility” is seeing quantum mechanics transform from theoretical physics into a basic force that is transforming information processing. Google and IBM have shown that quantum systems can solve scientific problems that traditional supercomputers cannot.

Beyond Binary: Qubit Power

The switch from bits to qubits introduces a “new computational grammar” at the heart of this transformation. Qubits explore massive solution areas using three fundamental quantum principles, unlike classical bits that are limited to 0 or 1. Superposition: Allows qubits to live in several states for concurrent data exploration. Entanglement: Connects qubits so that their states quickly affect one other for exceptionally efficient computation. Quantum algorithms use interference to boost correct responses and cancel out bad ones. These concepts allow quantum computers to solve “intractable” problems like the Variational Quantum Eigensolver (VQE) for simulating quantum systems and Shor's method for factoring large integers. Rise of Phys-Aware AI

While quantum hardware progresses, “physics-aware” AI models are revolutionizing software. Science used to see artificial intelligence (AI) as a “black box” of opaque systems that could predict trends but not “understand” the cosmos. AI has evolved beyond pattern recognition, a revolution. New model architecture explicitly includes physical limits. Unlike previous models that rectified flaws, these new models cannot break thermodynamics or fluid dynamics rules. Physics-integrated models outperformed numerical simulations by 100 in demanding situations like turbulent airflow predictions.

Resolving Scaling and Error

Despite quantum computing's promise, the industry is still in the Noisy Intermediate-Scale Quantum (NISQ) period, when technology is brittle and decoherent. Qubits are susceptible to ambient noise, making error correction difficult. To fix this, researchers are utilising machine learning (ML)-assisted decoders to reliably detect and fix logical circuit errors. New error-correcting codes announced in 2024 and 2025 will enable huge circuits with over a billion logic gates by the early 2030s. These scripts appear ten times more efficient than previous methods. Full-stack fault-tolerant designs are also being developed to co-optimize algorithms and hardware, reducing quantum computing stability overhead. Bringing Micro and Macro Together The major impact of these connected technologies is in chemistry and materials research. Atomic interactions that influence bridge strength and battery efficiency used to take weeks of supercomputer time to simulate. Modelling electromagnetic wavefunctions with Deep-Learning Density Functional Theory (DFT) and neural networks lets scientists transcend these sizes. This allows for unprecedented precision in material design, which could lead to superconductors or carbon-capture membranes. AI is also a “collaborative partner” conducting millions of internal “thought experiments” using synthetic data to uncover innovative fluid-structure interactions that may improve offshore wind turbine designs. Scientific Method 2.0: Ethics and Future As AI and quantum systems lead derivation and discovery, scientists are discussing “Scientific Method 2.0”. AI-generated hypotheses are verified instead of deduced in this new framework. An AI could find a new rule of physics with a billion-parameter proof, but human intuition is needed to set investigation parameters and judge societal consequences. Almost every industry is affected by this change: Medicine: Quantum-level molecular simulations speed drug discovery. Cybersecurity: Post-quantum cryptography is being promoted as quantum algorithms threaten conventional encryption. Finance: Optimising complex risk assessments and massive portfolios.

Conclusion: Innovation Catalyst

The JUPITER supercomputer breaks the quantum benchmark with its 50-qubit simulation.

50-Qubit Quantum Computer

The world's first full 50-qubit global quantum computer simulation was created by JUPITER, Europe's fastest supercomputer. German researchers at the Jülich Supercomputing Centre (JSC) and NVIDIA specialists attained this milestone, marking a new classical computing performance zone.

The finding is similar to a universal quantum computer with 50 qubits. JUPITER, Europe's first exascale supercomputer, is located in the Jülich Supercomputing Centre in Germany. The record of 48 qubits was set by Jülich physicists prior to this accomplishment. The achievement shows the September-unveiled JUPITER system's huge potential.

Significance of Quantum Simulation Milestone Simulating a 50-qubit system is essential for quantum technology advancement. Future quantum technologies are tested in quantum computer simulations. Researchers can use them to explore complex molecular modelling methods like the Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimisation Algorithm. Before quantum processors are reliable, researchers can test these algorithms at full scale using classical systems.

Prof. Hans De Raedt, the study's lead author, said JUQCS-50 can accurately mimic universal quantum computers. This lets them solve challenging issues that quantum processors cannot. The Universal Quantum Simulation of 50 Qubits on Europe's First Exascale Supercomputer's Heterogeneous CPU-GPU Architecture describes this achievement. Authors include Hans De Raedt, Jiri Kraus, Andreas Herten, Vrinda Mehta, Mathis Bode, Markus Hrywniak, Kristel Michielsen, and Thomas Lippert.

Breaking Classical Boundaries: Exponential Challenge Due to exponential resource costs, quantum circuit simulation on conventional computers is notoriously difficult. The simulation requires double the memory and processing power per qubit. Average laptops can only handle 30 qubits.

Simulating 50 qubits required tremendous resources. The simulation requires two petabytes of memory and complete orchestration of JUPITER's cutting-edge GH200 Superchips. Prof. Kristel Michielsen noted that only the world's largest supercomputers can supply that memory, demonstrating the close relationship between HPC and quantum research.

The simulation accurately simulates quantum processor mechanics. Each quantum gate action affects more than 2 quadrillion complex numbers, which must be synchronised across thousands of processing nodes on a scale that made simulations practically unfeasible.

Tech Innovations Drive Success

Innovative features of JSC's Jülich Universal Quantum Computer Simulator (JUQCS) led to the breakthrough. The new JUQCS-50 version takes advantage of the hybrid memory architecture and key features of the NVIDIA GH200 Superchips in the JUPITER supercomputer.

JUQCS-50 set the 50-qubit record using three major advances:

The application expands usable memory beyond GPU restrictions by using LPDDR5 memory and high-bandwidth CPU-GPU interconnects. This feature minimises performance loss when data is temporarily offloaded from GPU to CPU memory.

Adaptive Data Encoding: A novel byte-encoding compression technology greatly reduced memory use. This technique cuts memory needs by eight, with acceptable trade-offs in computational effort and precision.

An on-the-fly network traffic optimiser optimised data flow between the over 16,000 Superchips during the experiment.

These innovations accelerated performance by 11.4-fold over the K computer's 48-qubit record simulation.

Building Future Infrastructure

Jülich and NVIDIA engineers collaborated on the research under the JUPITER Research and Early Access Programme (JUREAP). Dr. Andreas Herten praised this early cooperation, which allowed JUPITER's software and hardware to be co-designed.

JUNIQ (Julich UNified Infrastructure for Quantum Computing) will add JUQCS-50, a powerful simulation tool. This connection gives outside scholars and corporations access to the technology. JUQCS-50 is expected to be a benchmark for next-generation supercomputer performance and a vital research engine.

Deep learning models and Variational Quantum Eigensolver (VQE) algorithms are used in the Hybrid Quantum–AI Framework for Protein Structure Prediction to overcome the disadvantages of each method alone. Our structure prediction method enhances accuracy and has applicability in near-term quantum computing by framing the task as “energy fusion.” Making structure prediction a “energy fusion” problem enhances accuracy and makes it suitable for near-term quantum computing (NISQ) devices.

The idea is to combine neural network data-derived biological priors with quantum algorithm physics-based modeling.

Below, we discuss the framework and its parts:

Considering restrictions requires a hybrid approach.

A challenging protein structure prediction starts with finding the lowest-energy conformation on a high-dimensional energy landscape.

AlphaFold3 and ColabFold limitations: These models are data-driven notwithstanding their success. They tend to use sequence alignments and huge training datasets instead of physical principles. This limits interpretability and generalizability, especially for brief peptide segments or conditions outside their training distribution.

VQE on NISQ devices' limitations: Quantum computing uses a novel physics-based paradigm to approximate a molecular Hamiltonian's ground-state energy. Noise, a paucity of qubits, and shallow circuit depth plague NISQ technology like the 127-qubit superconducting processor used in this study. This makes VQE predictions crude and often fail to reproduce fine-grained features like backbone dihedral angle distributions or secondary-structure motifs. The quantum energy landscape provides a reliable, low-resolution map.

Function of Fused Energy

For energy fusion, three normalized terms are weighted linearly combined:

Basic quantum-mechanical interactions and the conformational landscape's low-resolution global structure are reflected in normalized quantum energy.

Secondary Structure Distribution Divergence: This measure compares Ramachandran kernel-derived secondary structure probabilities from the quantum candidate's geometry to NSP3 predictions. It emphasises secondary structural pattern compliance.

The final metric, dihedral-angle consistency, measures how well the quantum candidate structure matches NSP3 predictions. This term encodes fine-grained backbone geometry.

Coefficients are user-specified trade-off weights that balance term contributions.

Importance and Results

Statistics showed statistically significant benefits for the hybrid framework over classical and quantum-only baselines.

The hybrid technique yielded a mean RMSD of 4.89 Å (median 4.70 Å) after analyzing 375 conformations from 75 protein fragments.

This accuracy lowers RMSD by 28.6%, ColabFold by 58.5%, and AlphaFold3 by 57.2% for predictions that are quantum-only.Significant improvements are shown by the Wilcoxon signed-rank and paired t-tests (p < 0.001).Practicality: Despite NISQ's low precision, the framework shows how data-driven models can benefit from it.

The fused energy score guides the re-ranking process towards structurally accurate, near-native conformations, as seen by its positive association with RMSD (R^2 = 0.322).

VQEzy Dataset Launches Variational Quantum Eigensolver Optimisation VQEzy Dataset

VQEzy, the first large-scale, open-source dataset for parameter initialisation, removes a major barrier to the practical implementation of Variational Quantum Eigensolvers (VQEs), a leading class of algorithms for the Noisy Intermediate-Scale Quantum (NISQ) era.

VQEzy, produced by Indiana University's Hui Min Leung and Fan Chen and the University of Central Florida's Chi Zhang, Mengxin Zheng, and Qian Lou, provides 12,110 VQE specs and full optimisation trajectories. In many-body physics, quantum chemistry, and related fields, VQE performance depends on initial parameters. Efficient parameter initialisation improves trainability and reduces suboptimal local minima. New machine learning-based parameter initialisers have shown groundbreaking performance, but a paucity of datasets has slowed their development. Overcoming Prior Research Limits The three primary drawbacks of the datasets available to academics make them unsuitable for machine learning training: (1) they were limited to a single domain; (2) they were modest, usually a few hundred instances; and (3) they often lacked complete coverage, missing ansatz circuits or optimisation trajectories. VQEzy was built to fix these difficulties. It is orders of magnitude larger and richer than previous datasets. Seven sample jobs with varying circuit implementations and qubit sizes cover the three core VQE application domains of quantum many-body physics, quantum chemistry, and random benchmarking. The optimised VQE parameter vector, thorough circuit specifications, problem Hamiltonians, and, most crucially, entire optimisation trajectories are available for each of the 12,110 examples in VQEzy. This rich data, including ground-state energy history, parameter dynamics, and barren plateau behaviour, makes VQEzy ideal for theoretical research and real-world VQE optimisation. The dataset is public and will be updated and expanded with user input. Diversifying Quantum Resources VQEzy was constructed using VQE optimisation, ansatz circuit selection, and problem Hamiltonian generation. Different Hamiltonians VQEzy contains applications from three main domains: Quantum many-body physics includes the one-dimensional Heisenberg XYZ (1D_XYZ), Fermi–Hubbard (1D_FH), and Transverse-Field Ising (2D_TFI) models. In all 4-qubit and 12-qubit cases, 1D_XYZ has 2000 parameter tuples. The 1D_FH model contributed 3,000 4, 6, and 8-qubit spin chains. Quantum chemistry has three molecular Hamiltonians. One bond length can yield 1000 combinations, while another can produce 150 and 160.

Random VQE: To avoid structural bias, 2800 four-qubit Hamiltonians were generated using random half-integer Pauli string coefficients. Selected Ansatz Circuits Ansatz selection affects VQE performance. The CZRXRY, highly entangling, and U3CU3 ansätze were employed for many-body physics, molecular Hamiltonians, and random VQE benchmarking, respectively. Normalised Optimisation The Adam optimiser with a learning rate of was employed in all VQE optimisation investigations since it balanced performance, computational cost, and GPU acceleration. Data for the first 12,110 instances was collected in over 200 hours using an AMD Ryzen 5 1600 CPU and an NVIDIA RTX 3090 GPU. Perspectives on Parameters Characterise the data using dimensionality reduction methods like Multidimensional Scaling (MDS) and t-distributed Stochastic Neighbour Embedding to provide insights into the optimised parameter space. Visualisations indicate that optimised parameter clusters may distinguish jobs and domains. The quantum many-body domain has separate parameter distributions for jobs like 1D_XYZ, 1D_FH, and 2D_TFI. Additionally, the 1D_FH model's optimised parameters displayed QAOA-like symmetry. As qubits increase, deeper symmetries like the 8-qubit O(2) symmetry create a more intricate environment. Analysis of optimised ground-state energies yielded important insights. The discretised Hamiltonian structure determines the energy modes of the random VQE application, while quantum many-body physics and quantum chemistry jobs use the qubit number. Future Uses and Growth In several research fields, the VQEzy dataset is advantageous: VQE Initialisation and Optimisation: It provides starting points that reduce initial loss and accelerate convergence for complex ML-based initialisation strategies across domains. Transfer Learning: Methodical parameter transferability and model-agnostic meta-learning investigations are now possible due to its large scope and variety of assignments.

VQC Variable Quantum Circuits

Introduced BVQC, a new backdoor-style watermarking mechanism to protect IP stored in Variational Quantum Circuits (VQCs), advancing quantum computing security. This novel approach addresses major flaws in previous watermarking methods to maintain the VQCs' original performance during regular operation while ensuring that the watermark is detectable and resistant to optimisation procedures like circuit re-compilation.

Variational Quantum Circuits (VQCs) are a powerful quantum computation paradigm because they can scale difficulties beyond traditional computers. These circuits encode solutions using a task loss function to minimise loss across a training dataset. VQCs are used in VQD, QAOA, and VQE.

Building VQCs involves expertise in robust encoding, hardware calibration, parameter tuning, and circuit ansatz, which is rare outside of quantum computing businesses. Leading quantum computing businesses sell their VQCs as valuable intellectual property in a growing industry due to their promise. A reliable watermarking solution is needed to validate VQC IP ownership since criminals may make and distribute unauthorised copies due to its commercial importance.

Current quantum circuit watermarking methods have two fundamental concerns. When circuits were recompiled, watermarks were removed. Many earlier techniques used suboptimal gate decomposition, qubit mapping, or gate scheduling as watermarks. Quantum compilers increase circuit fidelity and execution performance, hence unoptimised signature blocks should be removed during recompilation.

Approximation compilation eliminated several approaches, including random gates and rotation. Second, these techniques' lengthy inserted watermarks increased work loss, which reduced circuit accuracy, especially on noisy intermediate-scale quantum (NISQ) computers.

After re-compilation with state-of-the-art watermarking, Probabilistic Proof of Authorship (PPA) rose from Kolkata values, implying watermark removal. The Ground Truth Distance (GTD) for watermarked VQCs (using earlier techniques) increased from 0.036 to 0.107 on Kolkata and from 0.063 to 0.182 on Cairo, indicating accuracy degradation.

BVQC tackles these issues.

BVQC confronts these difficulties. The peculiarity is its backdoor-style embedding, which raises the loss to a specified amount during watermark extraction while keeping the original loss in normal execution circumstances. This is achieved by making BVQC a multi-task learning objective. The VQC learns to achieve high accuracy utilising base inputs and measurements for standard execution and to generate a predetermined watermark loss for a set of given inputs and measurements under watermark extraction conditions.

Predefined inputs, measurements, and losses are needed to develop a watermarked model. These elements are intentionally deviated from the basic input/measurement by modifying amplitude, phase, measurement bases, or weights.

Optimisation of the circuit to achieve the watermark set's loss and minimise base task loss.

Privacy for the preset set and public access to the well-trained VQC with the base set.

To prove ownership, the owner gives a third party the private input, measurement, and loss. Ownership is confirmed if the VQC delivers a loss equal to the predetermined (as opposed to typical circuit behaviour) under these settings.

The grouping algorithm in BVQC minimises watermark task interference to maximise base job accuracy. The system carefully selects inputs, measurements, and watermark losses by measuring the impact of gradient updates from the watermark work on the base job aim. In particular, it looks for setups where watermark gradient changes do not increase base task loss.

By aligning the two activities' optimisation routes, this cautious selection prevents performance deterioration. Without this categorisation, the base task GTD may differ significantly. The basic task's GTD value in BVQC, which averages 0.016 over 50 sample groups, shows the grouping algorithm's accuracy.

BVQC outperforms previous watermarking approaches.

BVQC reduces Probabilistic Proof of Authorship (PPA) changes, making it resistant to recompilation. BVQC-taught VQCs preserve watermarks, unlike other methods that raised PPA dramatically after re-compilation. BVQC uses parameter optimisation to put the watermark directly into the VQC's unitary matrix, which is mostly unaffected by compiler optimisations that focus on structural alterations.

Keep Accuracy: BVQC reduces Ground Truth Distance (GTD) by 0.089 compared to other watermarking approaches. BVQC yields lower task GTD than previous methods. The GTD of BVQC is similar to that of unwatermarked VQCs, implying that loss changes are modest. This shows that BVQC passes security criteria to maintain accuracy and prevent watermarking interference with VQC jobs. Preset inputs and measurements keep the average watermark GTD low, simplifying watermark extraction and distinguishing watermarked circuits from unwatermarked ones.

The study has made considerable strides, but the scientists recognise that strategies like fine-tuning the model with locally selected datasets are still needed to resist modern attacks. Current parameter smoothing methods reduce fine-tuning while retaining watermark persistence. Future study could increase the scheme's resistance to changing threats and investigate its application with more quantum computations.

Variational Quantum Algorithms Use Hamiltonian Expressibility to Unlock Quantum Potential.

Variable Quantum Algorithms

The rapidly emerging field of quantum computing may use Variational Quantum Algorithms (VQAs) to harness the power of near-term, noisy quantum devices, also known as Noisy Intermediate-Scale Quantum (NISQ) devices. Fusing quantum circuits with classical optimisation, these methods solve complex problems through parameter refinement.

The Variational Quantum Eigensolver (VQE) is designed to find a Hamiltonian operator's ground state. Quantum chemistry uses VQE to determine molecular ground-state energies and combinatorial optimisation using ground-state searches.

The Main Challenge: Selecting a Quantum Circuit In Variational Quantum Algorithms, choosing an effective parametric quantum circuit, or ansatz, is crucial. The ansatz controls quantum computation and affects the quality of the answer and the algorithm's trainability. A good ansatz should work with current quantum hardware, have a short circuit depth to reduce noise, and resist barren plateaus.

The Barren Plateau Issue

Variational Quantum Algorithms struggle with barren plateaus, which are cost function gradients that decrease exponentially with qubit count. This flattening of the optimisation landscape makes it difficult for classical optimisers to recognise meaningful parameter updates, especially for larger quantum systems, preventing convergence.

Even if a circuit needs exploratory power to find the ideal solution, too much power might lead to barren plateaus.

Introduce Hamiltonian Expressibility

Hamiltonian expressibility may help solve the barren plateau problem and ansatz selection. This metric assesses how evenly a circuit explores the problem's energy landscape as defined by a Hamiltonian. The idea is that a circuit's exploration capability should boost its chances of finding good answers. However, its potential to improve solutions has not been completely realised.

Researchers have a method to evaluate Hamiltonian expressibility for a given ansatz and Hamiltonian. VQE solves optimisation issues. A correlation analysis between the two metrics will determine if Hamiltonian expressibility improves Machine Learning for ansatz design.

Key Results: Ideal and Quiet

The study found several noteworthy things under ideal or extremely low noise conditions:

Ansatz Depth and Expressivity

Until saturation, a circuit's Hamiltonian expressibility improves with layer depth. Once saturation is attained, the expressibility value tends to swing inside a “maximally expressive zone” and more layers are no longer needed.

Problem-Specific Expressibility

Hamiltonian expressibility targets issues. Circuits often have higher expressibility than non-diagonal Hamiltonians for problems like Maximum Cut, Minimum Vertex Cover, Maximum Clique, and Random Diagonal. Because diagonal Hamiltonians have basis states as their best solutions, their energy space is naturally “narrower” and easier to explore.

Small-Scale Solution Quality Association

Small-scale problems (like four-qubit ones) are linked.

High Expressibility for Superposition Solutions: Ansätze with high Hamiltonian expressibility perform better for superposition-state and non-diagonal Hamiltonian problems. Classes like Heisenberg Superposition State and Random Non-Diagonal problems showed strong negative associations, meaning lower expressibility values lead to higher accuracy.

Basis-State Solutions Have Limited Expressibility: Circuits with low expressibility are better for problems having basis states, such as diagonal Hamiltonians. Extreme expressibility may backfire in some instances.

The link between expressibility and solution quality weakens as qubits increase, such as to eight. This evidence supports theoretical findings that barren plateaus, which can hinder trainability, have high expressibility.

Key Findings: Noise

Realistic quantum noise sources like decoherence and gate failures substantially modify the relationship between expressibility and solution quality, especially for small-scale settings (4 qubits).

Diagonal Hamiltonians and basis-state issues benefit more from low Hamiltonian expressibility than noiseless problems. This is because simpler, less expressive circuits are shallower and less noisy.

This relationship becomes more complicated for issues with superposition-state solutions and non-diagonal Hamiltonians. An intermediate level of expressibility may yield the best results for some superposition-state circumstances, such as the Heisenberg XXZ model, even if more expressive circuits are more noisy due to their complexity.

This requires balancing a circuit's exploration and noise resistance.

The study demonstrated a bell-shaped pattern in solution quality for some non-diagonal problems under noise: expressibility grows, peaks at an intermediate level, and then falls. This bell-shaped pattern appears when noise levels climb.

Conclusion and Future Plans

Variable Quantum Eigensolver

Hybrid Quantum Algorithm Revolutionises Simulation and Optimisation with VQE

Quantum computing has long been touted as a game-changer for solving complicated issues normal supercomputers cannot. The hybrid quantum-classical method Variational Quantum Eigensolver (VQE) is a prominent option for near-term quantum hardware. Chemistry, physics, and optimisation are changing because they can forecast molecular and material system ground state energies.

Variational Quantum Eigensolver?

In the early days of quantum computing, VQE was developed to determine a quantum system's ground state, or lowest energy state. Many scientific fields, including chemistry, require the ground state for stability and molecular structure. The hybrid VQE approach combines classical and quantum computing advantages:

Ansatz, or parametric quantum circuit, prepares candidate quantum states.

A classical optimiser adjusts this circuit's settings to reduce energy estimates based on measurements.

Shallow quantum circuits can approximate the ground state of molecular and material systems, making this technology excellent for early-stage, noisy quantum devices like NISQ processors. How VQE Works

Iterative feedback loops are crucial to VQE:

State Preparation: A parameterised quantum circuit encodes an estimated system wavefunction.

Measuring the system's energy involves measuring its Hamiltonian, an operator representing total energy.

Optimisation: Classical optimisation methods like gradient descent modify ansatz parameters after processing measurement data.

The algorithm iterates until it finds a set of parameters that yield the ground state's lowest estimated energy.

This hybrid technique allows quantum processors with few qubits to solve problems formerly reserved for computational chemists and theoretical physicists by avoiding noisy hardware.

Relevance in Quantum Computing

VQE is a critical technology for making quantum computing practicable in its early phases. It matters in several fields:

For material design and drug development, quantum chemistry delivers accurate molecular simulations and structure and reactivity data.

Design simulation of superconductor ground state energies.

Optimisation difficulties: The VQE framework can address combinatorial optimisation difficulties, making it suited for supply chain, logistics, and finance optimisation.

The approach is effective because it can be developed incrementally and uses current hardware. Even as hardware improves, researchers can easily migrate from noisy to error-corrected hardware platforms to VQE.

The VQE Evolution

Due to its usefulness and hardware-friendliness, the 2014 Variable Quantum Eigensolver algorithm became popular in quantum computing. Since then, researchers have recommended various improvements:

Ansatz Development: New ansätze improve state preparation accuracy and hardware efficiency.

Noise Mitigation: Measurement error correction and extrapolation improve noisy gear findings.

Advanced Optimisers: Traditional Optimisation approaches have improved VQE's ability to find global minima and avoid local minima.

Issues and Limitations

Despite its potential, VQE faces challenges:

Ansatz Design: Complex molecular systems make it challenging to develop an efficient state parameterisation (ansatz).

Barren Plateaus: Optimisation landscapes often have plateaus with little gradient information, making global minima difficult to find with typical optimisers.

Gate failures, decoherence, and qubit connectivity limit VQE accuracy and scalability.

Classical Optimisation Cost: Larger problems may require computationally expensive classical optimisation.

In conclusion

Early quantum computing approaches like the Variational Quantum Eigensolver can answer key scientific and practical problems. Its new hybrid method allows researchers to approximate ground-state energies using quantum hardware, making it essential in quantum chemistry, material design, and optimisation.