Today, more than sixty companies (in the world) are building quantum computers. The natural language of their quantum gates is that of linear algebra in a complex (Hilbert) vector space. Since 2017 it is known that one can replace the linear algebra with some string-rewriting rules no more complicated than the basic rules of arithmetic. The original system was introduced by Terry Rudolph and has been promoted and disseminated in large-scale outreach projects (among others) by Diana Franklin (University of Chicago), Sofia Economou and Ed Barnes (Virginia Tech) and other educators at the high-school level. In this workshop we show how a slightly modified (though still very elementary) system can be used to communicate a visual and entirely operational understanding of key quantum computation concepts such as: superposition, entanglement, phase, interference and unitary state evolution, as they occur in quantum algorithms. Examples include the phase kickback phenomenon, teleportation, and the famous Deutsch-Josza, Bernstein-Vazirani and Grover algorithms along with the GHZ game. We work out concrete examples of proving properties for quantum gates and quantum circuits without resorting at all to complex numbers or matrix multiplication; only simple, abacus-like operations are used, hence the title of the tutorial. We show how this approach can create a genuine bridge to the mathematics of quantum computation, that is, of vector and tensor algebras in complex spaces for students who may have little or no proper mathematical background.

At the time of this writing more than 60 (sixty) companies in the world are building quantum computers. These computers, based on quantum physics principles, are radically different from those that operate according to the more familiar principles of classical physics. A quantum algorithm takes a number of classical bits as its input, manipulates them so as to create a superposition of all their possible states, further manipulates this exponentially large superposition to obtain the final quantum result, and then measures the result to get (with the appropriate probability distribution) the same number of output bits as in its input. For the middle phase, there are elementary operations which count as one step and yet manipulate all the exponentially many amplitudes of the superposition. The natural language of these quantum gates is that of linear algebra in a complex (Hilbert) vector space. Since 2017 it is known that it is possible to replace the linear algebra with some string-rewriting rules which are no more complicated than the basic rules of arithmetic. The original system was introduced by Terry Rudolph and has been promoted and disseminated in large-scale outreach projects (among others) by Diana Franklin (University of Chicago) and Sofia Economou and Ed Barnes (Virginia Tech) as well as several other educators at the high-school level. In this paper we show how a slightly modified (though still very elementary) system can be used to communicate a visual and entirely operational understanding of key quantum computation concepts such as: superposition, probability, entanglement, phase, interference and unitary state evolution, as they occur in well-known quantum algorithms. We give concrete examples of proving properties for quantum gates and quantum circuits without resorting at all to complex numbers or matrix multiplication. Only simple, abacus-like operations are used-hence the title of the paper. The system we present allows a novice learner to actually trace a quantum algorithm as if it were a classical computation, which is a rare (and, frankly, borderline incredible) luxury in the area of quantum computation, where traditional debugging is impossible. Examples include the phase kickback phenomenon and the famous Deutsch-Josza algorithm. We end with a discussion (and more examples) of how this approach can create a genuine bridge to the mathematics of quantum computation, that is, of vector and tensor algebras in complex spaces for students who may have little or no proper mathematical background.

Continuing a process that began more than 50 years ago with the publication of Curriculum 68 ACM, IEEE-Computer Society and AAAI have sponsored five efforts to establish international curricular guidelines for undergraduate programs in computing on a roughly 10-year cycle. Over the last 15 years significant advances in quantum technologies have led to a new awareness about their impact on computing (QC). There are now 60 companies worldwide that build quantum computers. In the US the Quantum Economic Development Consortium (QED-C) was created in 2018 to accelerate the quantum industry by establishing a robust supply chain and infrastructure, including workforce and standards. But the continued absence of any serious education in quantum mechanics in a large fraction of traditional US engineering programs, including computer engineering and the closely related CS and data science programs, present many BS degree STEM graduates with the daunting problem of how to get trained quickly and efficiently to pursue the new opportunities in Quantum Information Science and Technology (QIST). To address this issue the ACM Board of Education has teamed up with the QED-C Workforce Development TAC and has developed (for the first time ever and over a period of 18 months) a Quantum Architectures (Q-AR) Knowledge Unit (KU) for CS2023. In November 2022 we asked the QED-C members (industry, academia, national labs, and government agencies) to comment on the proposed competency-based curricular plans along with the selected topics and learning outcomes. We present the analysis of the data we collected during that process.

In recent years we have witnessed a resurgence of learner-centered techniques in the college classroom--though not predominantly in the Computer Science classroom. Of all, Peer Instruction (PI) and Team-Based Learning (TBL) have proven themselves as the top two most successful evidence-based, interactive teaching methods. Both have been shown to cut failure rate in introductory classes by half (or more) while increasing self-reported learner satisfaction. However, as peer-based learning techniques with similar aims and outcomes but sharply different individual characteristics, they seem to be located at opposite ends of a spectrum for convenience of implementation. PI's minimal overhead in classroom management and overall setup is partly responsible for its recent, increasingly rapid, acceptance in Computer Science classes. TBL is a slightly more involved technique that however solves the free-riding problem: individuals participating in TBL develop a heightened sense of accountability and motivation. Neither can be reduced to the other, but in this paper I show that teams for TBL can be formed instantaneously during the first lecture in such an efficient and transparent way that it becomes comparable to the simplicity that makes PI so attractive. This process can be applied successfully prima facie (during the first day of class) to any group of any composition and size but is particularly effective if coupled with a series of classroom activities that can be used in the first lecture to jumpstart TBL in a CS0/CS1/CS2 type of course.