Perspectives of quantum computing for chemical engineering

Abstract

Quantum computing has been attracting public attention recently. This interest is driven by the advancements in hardware, software, and algorithms required for its successful usage and the promise that it entails the potential acceleration of computational tasks compared to classical computing. This perspective article presents a short review on quantum computing, how this computational approach solves problems, and three fields that quantum computing can potentially impact the most while relevant to chemical engineering: computational chemistry, optimization, and machine learning. Here, we present a series of chemical engineering applications, the developments, potential improvements with respect to classical computing, and challenges that quantum computing faces for each of these fields. This article intends to provide a clear picture of the challenges and potential advantages that quantum technology may yield for chemical engineering, together with an invitation for our colleagues to join us in the adoption and development of quantum computing.

Make Your Business Quantum-Ready Today

“…At this time, there are problems in pattern recognition via machine learning that are outside the capabilities of classical computers in science and engineering that would be solvable on a quantum computer. 15 The reason is that we anticipate that a quantum computer will readily identify unusual patterns for machine learning or other applications, which may well be beyond the ability of digital computers. Understanding the structure of such applications might encourage imaginatively identifying business opportunities well outside the scope of current-day problems.…”

Hybrid Classical-Quantum Optimization Techniques for Solving Mixed-Integer Programming Problems in Production Scheduling

“…Constraints ( 5) and (6) ensure that the machine processes multiple jobs sequentially rather than simultaneously. Constraints (7) and (8) are logical cuts that significantly narrow the search space of the problem [34]. The last two constraints enforce the nonnegativity of the start times tsj and the binary nature of the assignment and sequencing variables xjm and yjj'.…”

“…The feasible region of the job-shop scheduling problem can be relaxed by dropping the complicating constraints identified as the sequencing constraints in ( 5)- (8). The resulting relaxed MILP problem then comprises of the objective function in (1), subject to timing constraints in ( 2) and (3), assignments constraints in (4), and variable types in (9).…”

“…x and n j ts can be treated as parameters in the sequencing constraints ( 5)- (8). These inequalities then comprise the binary sequencing variables y only and can be formulated as a QUBO problem.…”

“…Variants of scheduling problems have also been tackled by quantum approaches using both gate-model [6] and annealing-based [7] devices. However, such approaches may not prove competitive until the inadequacies of QC, including the limited number of qubits and limited error correction capabilities, are overcome by further technological advancements [8]. To this end, several hybrid quantum-classical solution techniques that harness the complementary strengths of quantum and classical computers have been proposed to tackle scheduling problems [9,10].…”