Quantum Computing @ MIT: The Past, Present, and Future of the Second Revolution in Computing ∗ Francisca Vasconcelos, Massachusetts Institute of Technology, Departments of EECS and Physics very school day, hundreds of MIT students, faculty, and staff file into 10-250 for classes, Eseminars, and colloquia. However, probably only a handful know that directly across from the lecture hall, in 13-2119, four cryogenic dilution fridges, supported by an industrial frame, end- lessly pump a mixture of helium-3 and helium-4 gases to maintain temperatures on the order of 10mK. This near-zero temperature is necessary to effectively operate non-linear, anharmonic, super- conducting circuits, otherwise known as qubits. As of now, this is one of the most widely adopted commercial approaches for constructing quantum processors, being used by the likes of Google, IBM, and Intel. At MIT, researchers are work- ing not just on superconducting qubits, but on a variety of aspects of quantum computing, both theoretical and experimental. In this article we hope to provide an overview of the history, theoretical basis, and different imple- mentations of quantum computers. In Fall 2018, we had the opportunity to interview four MIT fac- ulty at the forefront of this field { Isaac Chuang, Dirk Englund, Aram Harrow, and William Oliver arXiv:2002.05559v2 [physics.pop-ph] 26 Feb 2020 { who gave personal perspectives on the develop- ment of the field, as well as insight to its near- term trajectory. There has been a lot of recent media hype surrounding quantum computation, so in this article we present an academic view of the matter, specifically highlighting progress be- Bluefors dilution refrigerators used in the MIT Engineering Quantum Systems group to operate superconducting qubits at ing made at MIT. near-zero temperatures. Credit: Nathan Fiske ∗[email protected] Quantum Computers: A Brief History computation. Additionally, David P. DiVincenzo, then at IBM Research, listed the five main requirements to realize The notion of a quantum computer was first introduced a physical quantum computer [8]. Among these included by Caltech Professor (and MIT alumnus) Richard Feyn- the challenges of isolating quantum systems from their man in his 1960 \There's Plenty of Room at the Bot- noisy environments and accurately controlling unitary tom" lecture [1], suggesting the use of quantum effects transformations of the system. 1997 witnessed the first for computation. However, it was not until the late 1970s publications of papers realizing physical gates for quan- that researchers truly began exploring the idea. By May tum computation, based on nuclear magnetic resonance 1980, Paul Benioff { then a researcher at the Centre de (NMR) [9, 10]. Three out of the five authors on these Physique Thorique, CNRS { published the first article transformative papers (Isaac Chuang, Neil Gershenfeld, on quantum computation, \The computer as a physical and David Cory) are current or previous MIT faculty. system: A microscopic quantum mechanical Hamiltonian Additionally, that year, two new physical approaches to model of computers as represented by Turing machines", quantum computing were proposed, making use of ma- in the Journal of Statistical Physics [2]. In the same year, jorana anyons [11] and quantum dots [12]. In 1998, the Russian mathematician and Steklov Mathematical Insti- University of Oxford, followed shortly after by a collabo- tute faculty, Yuri Manin published the book \Computable ration between IBM, UC Berkeley, and the MIT Media and Uncomputable" [3], motivating the development of Lab, ran the Deutsch-Jozsa algorithm on 2-qubit NMR quantum computers. In 1981, MIT held the very first devices [13]. These served as the very first demonstra- conference on the Physics of Computation at the Endi- tions of an algorithm implemented on a physical quantum cott House [4], where Benioff described computers which computer. This was soon followed by the development of could operate under the laws of quantum mechanics and a 3-qubit NMR quantum computer, a physical implemen- Feynman proposed one of the first models for a quantum tation of Grover's algorithm, and advances in quantum computer. Clearly, MIT had roots in the field from its annealing. Thus, by the end of the twentieth century, re- conception. searchers demonstrated the physical potential of quantum In 1992, David Deutsch and Richard Josza, proposed computing devices. the Deutsch-Josza algorithm [5]. Although the algorithm The turn of the century would mark a transition of in itself was not very useful, a toy problem some may say, focus towards improving and scaling these devices. The it was one of the first demonstrations of an algorithm early 2000s alone witnessed the scaling to 7-qubit NMR that could be solved far more efficiently on a quantum devices, an implementation of Shor's algorithm which computer than on a classical (or non-quantum) computer. could factor the number 15, the emergence of linear op- This idea, that quantum computers may exhibit a bet- tical quantum computation, an implementation of the ter computational complexity than classical computers, Deutsch-Jozsa algorithm on an ion-trap computer, and is known as quantum advantage. In the years that fol- the first demonstration of the quantum XOR (referred lowed, more work was done in the domain of quantum to as the CNOT gate). The following years consisted algorithms and in 1994 one of the biggest results in the of similarly remarkable developments in and scaling of field was achieved. Current MIT Professor of Applied quantum computation technology. In fact, this growth Mathematics, Peter Shor, who was then working at Bell has been so impressive, that it is reminiscent of an expo- Labs, proposed the now well-known Shor's algorithm [6]. nential Moore's-Law-type growth in qubit performance. This quantum algorithm reduced the runtime of integer This progress has resulted in excitement for quantum number factorization from exponential to polynomial time. computation far beyond the realm of academia. As men- While this may seem like a fairly abstract problem, the tioned earlier, several large tech companies now have assumption that integer factorization is hard is the foun- well-established research divisions for quantum technolo- dation of RSA security, the primary cryptosystem in use gies and the number of quantum startups seem to double today. As the first algorithm to demonstrate significant each year. Furthermore, there has been a large increase in quantum advantage for a very important problem without interest from both the government, with the approval of obvious connection to quantum mechanics (like quantum a $1.2 billion Quantum Initiative Act in 2018 one of the simulation), Shor's algorithm caught the attention of the only bipartisan legislative acts passed in recent memory government, corporations, and academic scientists. This { and the general public, with Google's recent announce- created major interest in the field of quantum information ment of quantum supremacy [14]. However, with this and gave experimental researchers a strong justification increased interest and the desire for easily-approachable to develop quantum computers from the hardware end. explanations to complex research comes the tendency to The years following the announcement of Shor's al- hype the current state of the field. Thus, we interviewed gorithm witnessed significant developments in quantum four faculty at the forefront of academic research in quan- computation, from both the theoretical and experimental tum information and computation for their outlooks, to perspectives. In 1996, Lov Grover of Bell Labs developed see if we could make sense of where all these quantum the quantum Grover's algorithm [7], which provides a technologies are headed in the near- and long-term. quadratic speedup for search in unstructured problems, using a technique now known as amplitude amplification. That same year, the US Government issued its first pub- lic call for research proposals in the domain of quantum information, signifying the growing interest in quantum December 2019 Quantum Computing @ MIT page 2 of 19 Professor William Oliver – Superconducting Quantum Processors William Oliver is the current Associate Director of the MIT Research Laboratory of Electronics (RLE), an As- sociate Professor of EECS, a Physics Professor of the Practice, and a Fellow of the MIT Lincoln Laboratory. He is also a Principal Investigator in the Engineering Quan- tum Systems Group (MIT campus) and the Quantum Information and Integrated Nanosystems Group (MIT Lincoln Laboratory), where he leads research on the ma- terials growth, fabrication, design, and measurement of superconducting qubits. How would Prof. Oliver explain his research to a non-expert? When we asked Professor Oliver to explain his research in general terms, he described superconducting qubits as “artificial atoms." Like natural atoms, they have discrete quantum energy levels, in which transitions can be driven. One key difference, however, is that these artificial atoms are macroscopic electrical circuits comprising an Avo- grado's number of actual atoms. Given that a qubit is a circuit, its energy levels can be engineered to be more Professor William Oliver, PI of the MIT Engineering Quantum optimally suited for quantum computation. Systems group. Credit: Nathan Fiske At a high level, a superconducting qubit is an LC-circuit solid-state systems to demonstrate quantum mechanical { an inductor and capacitor in parallel { like you might behavior. Although they were not long-lived enough for see in an E&M course, such as 8.02. This type of circuit quantum computation, electrons in 2D electron gasses is a simple harmonic oscillator with a harmonic potential, were sufficiently coherent to do the experiments he was meaning an equal energy spacing between all the energy interested in at the time. Professor Oliver claims that, levels. However, quantum bits are generally built from during his PhD, he was motivated solely by blue-sky the ground and first excited energy levels, correspond- physics rather than engineering applications like quantum ing to the two possible bit states (0 and 1).