Update March 4, 2024: We are close to being able to reveal BEAST-VQE, the successor of FAST-VQE, which will allow us to double the complexity of the systems that we can address, and enable us to take a new leap in what is possible with current quantum hardware.
The Next Chapter in the NISQ era - Quantum Computing Hardware Exploration
We aim to advance the field of quantum computing, committing to foundational research and staying at the innovation forefront, in collaboration with partners, clients and through our product development on quantum-relevant problems.
Our commitment to pushing the boundaries is exemplified by Kvantify's novel FAST-VQE algorithm, which presents itself as an exceptionally adept and practical algorithm for NISQ devices, delivering dependable performance and cost-effectiveness for electronic structure calculations of small molecules.
In an ongoing effort to push the bar and test current quantum computing hardware, we have been running benchmark calculations for H_4 and LiH molecules. Today we are ready to share the results, in an AWS blog post.
By combining the quantum-classical hybrid algorithm FAST-VQE with Amazon Braket Hybrid Jobs, we have demonstrated the feasibility of conducting practical and accurate electronic structure computations on current NISQ devices.
Quantum computing’s potential for computational chemistry is immense, but practical limitations persist. Recent advances in hybrid algorithms and noise reduction, along with platforms like Amazon Braket, are narrowing the gap.
Our team is working on several practical uses for the FAST-VQE algorithm, and are looking forward to sharing more news with use cases!
Brief conclusion from the full blogpost:
By combining the quantum-classical hybrid algorithm FAST-VQE with Amazon Braket Hybrid Jobs, we have demonstrated the feasibility of conducting practical and accurate electronic structure computations on current NISQ devices. FAST-VQE fixes the problem of operator selection in adaptive VQE algorithms, which is believed to be the main bottleneck of adaptive VQE. However, other bottlenecks exist, primarily regarding energy optimization which is still an outstanding problem. In future work, we will evaluate FAST-VQE for larger systems to further benchmark its performance.
Our results enhance chemical precision efficiently while requiring fewer resources. Additionally, our theoretical findings demonstrate how the algorithm can scale to molecular sizes that were previously considered difficult to attain. FAST-VQE takes an important step towards practical applications of noisy quantum computers in chemistry and drug-discovery.
For more information on our quantum-ready solutions, please do not hesitate to reach out at contact@kvantify.dk
Figure 1 -- Comparison of energy errors for VQE variants on H4 and LiH, using a STO-3G basis set on simulators and quantum devices. The operator selection part of FAST-VQE is run as a finite shot state vector evolution simulation, a density matrix simulation, Rigetti’s Aspen-M-3 and IonQ’s Harmony (the latter only for H4). The density matrix simulation includes realistic noise. For comparison, a finite shot state vector simulation is also shown for ADAPT-VQE. For all calculations the energy optimization was done as a state vector simulation without noise. All plots include 95% confidence intervals. Note how ADAPT-VQE does not converge to chemical accuracy within the range of figure, while all cases of the FAST-VQE converge at around 150 CNOT. In the case of H4, this is equivalent to 15 adaptive iterations. For LiH, all cases of FAST-VQE converge within 100 CNOTs equivalent to less than 15 adaptive iterations.
