  Quantum computing is an information processing paradigm that uses
quantum-mechanical properties to speedup computationally hard problems.
Although promising, existing gate-based quantum computers consist of only a few
dozen qubits and are not large enough for most applications. On the other hand,
existing QAs with few thousand of qubits have the potential to solve some
domain-specific optimization problems. QAs are single instruction machines and
to execute a program, the problem is cast to a Hamiltonian, embedded on the
hardware, and a single quantum machine instruction (QMI) is run. Unfortunately,
noise and imperfections in hardware result in sub-optimal solutions on QAs even
if the QMI is run for thousands of trials.
  The limited programmability of QAs mean that the user executes the same QMI
for all trials. This subjects all trials to a similar noise profile throughout
the execution, resulting in a systematic bias. We observe that systematic bias
leads to sub-optimal solutions and cannot be alleviated by executing more
trials or using existing error-mitigation schemes. To address this challenge,
we propose EQUAL (Ensemble Quantum Annealing). EQUAL generates an ensemble of
QMIs by adding controlled perturbations to the program QMI. When executed on
the QA, the ensemble of QMIs steers the program away from encountering the same
bias during all trials and thus, improves the quality of solutions. Our
evaluations using the 2041-qubit D-Wave QA show that EQUAL bridges the
difference between the baseline and the ideal by an average of 14% (and up to
26%), without requiring any additional trials. EQUAL can be combined with
existing error mitigation schemes to further bridge the difference between the
baseline and ideal by an average of 55% (and up to 68%).
