Quantum machine learning: a classical perspective

Abstract

Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning (ML) techniques to impressive results in regression, classification, data generation and reinforcement learning tasks. Despite these successes, the proximity to the physical limits of chip fabrication alongside the increasing size of datasets is motivating a growing number of researchers to explore the possibility of harnessing the power of quantum computation to speed up classical ML algorithms. Here we review the literature in quantum ML and discuss perspectives for a mixed readership of classical ML and quantum computation experts. Particular emphasis will be placed on clarifying the limitations of quantum algorithms, how they compare with their best classical counterparts and why quantum resources are expected to provide advantages for learning problems. Learning in the presence of noise and certain computationally hard problems in ML are identified as promising directions for the field. Practical questions, such as how to upload classical data into quantum form, will also be addressed.

Stimulating Intrinsic Motivation in Millennial Students: A New Generation, a New Approach

“…These tools can be easily adapted to represent a very wide variety of client conditions (Miller, ; Kusurkar, ) that can be t ailored to different levels of difficulty ( Strombach et al, ; Raza et al, ) . Moreover, simulation offers students the opportunity to observe their individual actions (Raab, ; Khalil et al, ), repeat training exercises (Ciliberto et al, ; Khalil et al, ), and most importantly learn from their mistakes (Archer, ; Augustyniak et al, ). For educators, the use of simulation offers opportunities to provide feedback during the training (Worly et al, ; Kusurkar, ) and debriefing after the sessions (Haro et al, ; Min, ).…”

“…Their teaching was thus primarily led by an educator (Smith and Shoffner, 1991;Orsini et al, 2015), and student input was very limited (Graf et al, 2007;Havyer et al, 2017). This practice of teaching was accepted by students belonging to the Baby Boomers and Gen-X generations (Strombach et al, 2016;Ciliberto et al, 2018) who have a high respect for hierarchy (Gibson, 2018), and a willingness to yield to authority (Buetow, 2007;Walker et al, 2016;Manzi et al, 2017). However, the current generations of Millennials (Rothmaler et al, 2017) and Centennials (Amini et al, 2018) expect a more equitable treatment due to lesser regard for a strict chain of command (Gonzalo et al, 2016;Rowland and Kumagai, 2018).…”

“…Millennial students think (Brass et al, 2017;Mabel and Britton, 2018) and react differently (Kong et al, 2014;de Paiva et al, 2017;Ciliberto et al, 2018) due to their upbringing (Tanaka et al, 2009;Palmen et al, 2015), and the influences of the rapid advancements in technology (Budden et al, 2017;Luthar, 2017). They differ from previous generations of students in that monetary gains are not their primary motivation for studies (Wilkinson et al, 2014;Shatto and Erwin, 2017), and personal development (Keister et al, 2017;Burch et al, 2018;Chung et al, 2018), and study/life balance are equally important to them (Chung et al, 2018;Sahoo and Mohammed, 2018).…”

“…Many experts believe that such skills are taught most effectively by trained clinicians (Barie and Ho, 2012;Cianciolo et al, 2016), since it conveys the tacit message to students that acquisition of these skills is a goal in itself and not just a tool to provide a higher quality of client care (Mohr et al, 2011;Christensen et al, 2018). Again, the extent of the success of this teaching practice is determined by the competence (Chen et al, 2017;Ciliberto et al, 2018), knowledge (Havyer et al, 2017;Fischer et al, 2019), and the overall humanistic attitude of the educator (Joshi et al, 2017;Oommen et al, 2017;Mestdagh et al, 2018).…”

“…However, excessive reliance should not be placed on its utility as a teaching approach, since simulation by itself does not guarantee learning as it cannot provide the full human experience (Havyer et al, 2017). Most significantly, it is limited by knowledge acquired in the classroom (Strombach et al, 2016;Ciliberto et al, 2018), and therefore cannot entirely allay the apprehensions of students in their transition to reality (Abrishami, 2018;Englander and Carraccio, 2018). Another caveat often advanced in the incorporation of simulation-based training is that it entails excessive expenditure in its setup (Yan et al, 2017;Manzi et al, 2018) and maintenance (Gliddon and Cridge, 2015;Pandey et al, 2018;Pape et al, 2018).…”