Variational Quantum Algorithms (VQAs) rely upon the iterative optimization of a parameterized unitary circuit with respect to an objective function. Since quantum machines are noisy and expensive resources, it is imperative to choose a VQA's ansatz appropriately and its initial parameters to be close to optimal. This work tackles the problem of finding initial ansatz parameters by proposing CAFQA, a Clifford ansatz for quantum accuracy. The CAFQA ansatz is a hardwareefficient circuit built with only Clifford gates. In this ansatz, the initial parameters for the tunable gates are chosen by searching efficiently through the Clifford parameter space via classical simulation, thereby producing a suitable stabilizer state. The stabilizer states produced are shown to always equal or outperform traditional classical initialization (e.g., HartreeFock), and often produce high accuracy estimations prior to quantum exploration. Furthermore, the technique is classically suited since a) Clifford circuits can be exactly simulated classically in polynomial time and b) the discrete Clifford space, while scaling exponentially in the number of qubits, is searched efficiently via Bayesian Optimization. For the Variational Quantum Eigensolver (VQE) task of molecular ground state energy estimation up to 20 qubits, CAFQA's Clifford Ansatz achieves a mean accuracy of near 99%, recovering as muchmore »
Logical abstractions for noisy variational Quantum algorithm simulation
Due to the unreliability and limited capacity of existing quantum computer prototypes,
quantum circuit simulation continues to be a vital tool for validating next generation quantum computers and for studying variational quantum algorithms,
which are among the leading candidates for useful quantum computation.
Existing quantum circuit simulators do not address the common traits of variational algorithms, namely:
1) their ability to work with noisy qubits and operations,
2) their repeated execution of the same circuits but with different parameters, and
3) the fact that they sample from circuit final wavefunctions to drive a classical optimization routine.
We present a quantum circuit simulation toolchain based on logical abstractions targeted for simulating variational algorithms.
Our proposed toolchain encodes quantum amplitudes and noise probabilities in a probabilistic graphical model,
and it compiles the circuits to logical formulas that support efficient repeated simulation of and sampling from quantum circuits for different parameters.
Compared to stateoftheart state vector and density matrix quantum circuit simulators,
our simulation approach offers greater performance when sampling from noisy circuits with at least eight to 20 qubits and with around 12 operations on each qubit,
making the approach ideal for simulating nearterm variational quantum algorithms.
And for simulating noisefree shallow quantum circuits with 32 qubits, our simulation approach offers a 66X reduction in more »
 Publication Date:
 NSFPAR ID:
 10286343
 Journal Name:
 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS ’21)
 Page Range or eLocationID:
 456 to 472
 Sponsoring Org:
 National Science Foundation
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