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Variational Quantum Eigensolver #10
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@m24639297 Does this project need a coach? Or, do you already have one. If so, please specify coach's github handle above. Thanks! |
VQE is already available as part of Aqua. What do you want to implement in particular? |
Though VQE has been implemented in Aqua, building a VQE on one's own could be helpful for beginners to understand the physics and notion behind it. Besides, as mentioned in the description, circuit design, measurement strategy, and error mitigation etc are all important to VQE and are still under studying. Any of the new-proposed ideas can be implemented if participants regard it potentially useful. |
May I join in this project? thanks |
I am an undergrad student from NCKU, may I join this team? |
I have some interest in this topic, can I join it? |
@m24639297 can we find somewhere to gather all people and discuss our issue? |
@starktech23 can u add me in issue thanks! |
i'm in |
Abstract
Implementing variational quantum eigensolver(VQE) for different problems, e.g. chemical ones, on both simulator and IBMQ quantum processor.
Description
Quantum computing has been considered to possess the capability of tackling some classically intractable problems such as quantum many-body simulation. However the reliability and scalability of the state-of-the-art quantum hardware, called Noisy Intermediate-Scale Quantum(NISQ) device, has not reached the threshold to implement any useful pure quantum algorithms. Thus in the NISQ era, hybrid-quantum-classical(HQC) algorithms are getting popular, where variational quantum eigensolver(VQE) is one of the currently applicable methods[1].
VQE consists of four main parts including Hamiltonian definition, ansatz design, measurement, and optimization. For Qiskit beginners, as the first step they may try to implement a VQE program from scratch for some pre-defined problem such as molecular energy calculation. Moreover, the design of ansatz(e.g. hardware-efficient ones[2] or problem-specific ones such as UCCSD[3]), strategy for efficient measurement[4], ways of optimization(e.g. gradient-based[5] or not) are all open to question. Participants are encouraged to discuss or study about any aspect of the VQE procedure.
Reference
[1] Nature communications 5, 4213 (2014)
[2] Nature 549, 242-246(2017)
[3] Nature communications 5, 4213 (2014)
[4] J. Chem. Phys. 152, 124114 (2020)
[5] Phys. Rev. A 99, 032331 (2019)
Members
Deliverable
Nature 549, 242-246(2017)
Qiskit Aqua: VQE module
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